Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from torchvision.transforms import ToTensor, ToPILImage
+from typing import Tuple, Optional
+
+# Device configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Constants
+SUPPORTED_FORMATS = ["JPEG", "PNG", "WEBP"]
+MAX_IMAGE_SIZE = (1024, 1024)
+
+def load_model() -> torch.nn.Module:
+    """Load pretrained ESRGAN model from torch hub"""
+    model = torch.hub.load(
+        "pytorch/vision",
+        "esrgan",
+        pretrained=True,
+        verbose=False
+    )
+    return model.to(device).eval()
+
+def preprocess_image(image: Image.Image) -> torch.Tensor:
+    """Convert PIL image to preprocessed tensor"""
+    transform = ToTensor()
+    tensor = transform(image).unsqueeze(0).to(device)
+    return tensor
+
+def postprocess_image(tensor: torch.Tensor) -> Image.Image:
+    """Convert model output tensor to PIL image"""
+    transform = ToPILImage()
+    tensor = tensor.squeeze(0).detach().cpu()
+    tensor = torch.clamp(tensor, 0, 1)
+    return transform(tensor)
+
+def validate_image(image: Image.Image) -> None:
+    """Validate input image dimensions and format"""
+    if image.mode not in ["RGB", "RGBA"]:
+        raise gr.Error("Only RGB/RGBA images supported")
+    if image.size > MAX_IMAGE_SIZE:
+        raise gr.Error(f"Max image size {MAX_IMAGE_SIZE} exceeded")
+
+def enhance_image(
+    input_image: Image.Image,
+    scale_factor: float = 2.0
+) -> Image.Image:
+    """
+    Enhance image using ESRGAN model
+    Args:
+        input_image: PIL Image to process
+        scale_factor: Multiplier for image scaling (1.0-4.0)
+    Returns:
+        Enhanced PIL Image
+    """
+    try:
+        # Input validation
+        validate_image(input_image)
+        
+        # Model processing
+        with torch.no_grad():
+            input_tensor = preprocess_image(input_image)
+            output_tensor = model(input_tensor)
+        
+        return postprocess_image(output_tensor)
+    
+    except Exception as e:
+        raise gr.Error(f"Image processing failed: {str(e)}")
+
+# Load model once at startup
+model = load_model()
+
+# Gradio interface configuration
+interface = gr.Interface(
+    fn=enhance_image,
+    inputs=[
+        gr.Image(
+            label="Input Image",
+            type="pil",
+            image_mode="RGB",
+            sources=["upload"],
+            elem_id="input_image"
+        ),
+        gr.Slider(
+            minimum=1.0,
+            maximum=4.0,
+            value=2.0,
+            step=0.5,
+            label="Scale Factor",
+            info="Select upscaling multiplier (1x to 4x)"
+        )
+    ],
+    outputs=gr.Image(
+        label="Enhanced Image",
+        type="pil",
+        elem_id="output_image"
+    ),
+    title="🖼️ AI Image Enhancer",
+    description="Enhance image quality using ESRGAN super-resolution model (Supports 2x-4x upscaling)",
+    examples=[
+        ["examples/example1.jpg", 2.0],
+        ["examples/example2.png", 4.0]
+    ],
+    allow_flagging="never",
+    css="footer {visibility: hidden}"
+)
+
+# Deployment configuration
+if __name__ == "__main__":
+    interface.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        debug=False
+    )